Conference paper
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
FLOOD MAPPING USING INSAR COHERENCE MAP
Slim Selmia , Wajih Ben Abdallaha , Riadh Abdelfattaha,b
a
COSIM Lab, University of Carthage, Higher School Of Communications of Tunis
Route de Raoued KM 3.5, Cite El Ghazala Ariana 2083 - Tunisia, email:[email protected]
b
D´epartement ITI, T´el´ecom Bretagne, Institut de T´el´ecom, Technopˆole Brest-Iroise CS 83818
29238 Brest Cedex 3 - France, email:[email protected]
KEY WORDS: Flood detection, SAR Data, InSAR coherence map.
ABSTRACT:
Classic approaches for the detection of flooded areas are based on a static analysis of optical images and/or SAR data during and after
the event. In this paper, we aim to extract the flooded zones by using the SAR image coupled with the InSAR coherence. A new
formulation of the ratio approach for flood detection is given considering InSAR coherence. Our contribution is to take advantage from
the coherence map provided using the InSAR pairs (one before and one after the event) to enhance the detection of flooded areas. We
explore the fact that the coherence values during and after the flood are mainly differents on the flooded zones and we give a more
suitable flood decision rule using this assumption. The proposed approach is tested and validated in the case of the flood taken place in
2005 in the region of Kef in Tunisia.
1.
INTRODUCTION
Earth has experienced in recent years, several natural disasters
that have an environment impact as well as various aspects of human life (Zhou et al., 2000). One of these remarkable events is
floods. In Tunisia, the risk of floods affects about two out of three
municipalities (of the Directorate General of Water Resources,
2005). It is therefore necessary to provide objective and comprehensive information about the area before, during and after
this hydrological event(Zhou et al., 2000). To guide support effectively and to provide quantifiable estimates of affected infrastructure and land extent, remote sensing techniques are used to
measure and monitor floods (Andreoli and Yesou, 2007). Indeed,
remote sensing data integration allows calculation and rapid assessment of water levels, damages and flood risk by area (Zhou
et al., 2000), (Andreoli and Yesou, 2007).
In this study, two main methods based on SAR (Synthetic Aperture Radar) data, for the extraction of flooded areas are studied
and compared. We then propose an optimization of the more
accurate one: the ratio approach (Andreoli and Yesou, 2007).
The first method is developed by NEST (Next ESA, all rights
reserved, n.d.) and it is based on the determination of a mask
from the color composite images taken before and during the
flood. Andreoli and Yesou developed an approach based on the
texture filtering (Andreoli and Yesou, 2007) and the detection of
the change through the bands ratio (Andreoli and Yesou, 2007).
In order to overcome the problem of decorrelation due to the
landslide caused by the flood, we used the InSAR coherence as
an additional information for texture filtering. Thus, flood mapping using ETM+ optical data is considered as a ”ground truth”
(Van Leer and Galantowicz, n.d.) for the validation and comparison of these methods.
2.
FLOODS IN NORTHWESTERN OF TUNISIA
We are interested in the region of Kef in the north-west of Tunisia
Fig. 1. This region is knowing for having a semi-arid climate and
it becomes inaccessible after floods. This will make very difficult
the task of research looking for exploring the test site within field
campaign after a flooding.
Figure 1: Location of the study area (36◦ 10′ 55′′ N, 8◦ 42′ 52′′ E).
According to the Tunisian hydrological calendar, published by
the Ministry of Agriculture and Water Resources (of the Directorate General of Water Resources, 2005), February is the month
of greatest rainfall. Table 1 shows data on precipitation in 2005.
Floods in Kef is taken place between February 14th and 15th2005 .
Researchers took more then 2 months (until April) in order to get
on the test site. This mainly due to the landslides caused by the
flooding.
In order to get a priori estimation of the flood mappping, we propose to explore different approaches based on SAR data. In this
paper, flood mapping using optical data will serve as reference
for the validation of results given from SAR approaches.
3.
FLOOD MAPPING USING OPTICAL DATA
A classic way to detect the flooded areas is based on the analysis of the optical images. The main idea is to test different processing satellite data to extract thematic land such as principal
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
161
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
Table 1: The amount of rain in 2005 in three rainfall stations in
the region of Kef.
Station
Mellegue
Zebida
K. Khasba
Jan
41.0
42.0
42
Feb
65.0
68.0
63.0
Mar
34.8
22.8
27.0
Apr
53.0
34.0
40.0
May
8.0
0.0
6.0
Jun
23.6
14.0
77.0
Jul
4.8
4.0
47.0
component analysis, the colored composition, multispectral classification and the band ratio 4/3 (Roque et al., 2013). Because
the Kef test site is mainly composed of soil, vegetation and water
(Roque et al., 2013), we opted for the choice of the band ratio
4/3 since it maximizes classes separability (of Environment and
of Life Branch, 2006).
The use of Landsat ETM+ tool for images processing and analysis has identified the most relevant elements of the landscape and
draw up land using maps (Fig. 2).
For our study, we used the Lee filter (Lee et al., 1998) to reduce
speckle noise and NEST tool (Next ESA, all rights reserved, n.d.)
to process image data and metadata. The radar sensors have a
high sensitivity to the dielectric constant and the surface roughness of observed objects. If we assume that a free water surface
has a very low roughness, we can consider it as specular reflector.
Thus, water is distinguished with a very low image backscatter
(Roque et al., 2013)(Silveira and Heleno, 2008).
4.1.1 Detection of flood using a mask To measure the extent
of floods, the permanent water surfaces shouldn’t be considered.
Otherwise, we will get erroneous results. This implies that we
need to hide some pixels and expose others. Before creating a
mask, the RGB image will be used where the red band denotes
the crisis image (February), the green band denotes the archive
1 and the blue band is or the archive 2 (Next ESA, all rights reserved, n.d.). From these images, we can see pixels associated
with flooded areas colored in purple. Knowing these values, and
based on histograms of images taken before and after the floods,
thresholds could be defined. Therefore, the permanent water surfaces could be detected using these thresholds. Using these data,
we can notice that the pixel values, in the case of the crisis image
(14 February), are below thresholds (from 250 to 350) but in the
case of the picture archive, the same pixels values are between
400 and 600. So the flooded areas satisfy the condition of N
mask (Fig. 3):
Figure 2: Optical image of river Mellegue taken by Landsat
ETM+ satellite on Mai 2005 with band ratios 4/3.
The pixels having values below 1.76 denote the surfaces of water.
Among water regions, some aren’t related to flooding (overflow
from the river due to the high flood waters and stagnant rain).
By examining the optical images taken over the region of Kef in
Tunisia on Marsh and May 2005, we can detect the flooded areas. The resulting map shows the areas that are affected during
the crisis. This will save for us as a ground truth for validation of
the developed approach. During the flood(in February) and immediately after the flood, optical data are not exploitable. Then,
SAR data in such situation are more adapted for flood mapping.
4.
FLOOD MAPPING USING SAR DATA
The radar data set used in this paper is provided by Envisat-2
satellite covering northwest of Tunisia and taken between February 2005 (during the flood) and May 2005 (after the flood).
4.1
Methodology
The methodology of radar data processing consists of three steps
(Andreoli and Yesou, 2007)(Silveira and Heleno, 2009):
• Geo-location of radar images
Figure 3: Flooded area (in red) around river Mellegue generated
with Envisat satellite image on Mars 2005 using NEST method.
N:
• Image interpretation and information extraction.
It < 350
It+1 > 550
(1)
where It and It+1 are the Envisat images taken during and after
the flood event repectively.
4.1.2 Detection of flooding using the ratio method The changing detector chosen for this method is based on the improvement
of the ratio that includes two levels of analysis (Roque et al.,
2013), the macro-scale analysis based on a first ratio r1 in order
to highlight changes of large homogeneous areas and combined
with a second ratio r2 obtained from two raw images that highlight key changes, while keeping a thematic accuracy of the raw
data (Roque et al., 2013)(Rignot and van Zyl, 1993).
r1 =
• Radiometric processing
I1,t+1
I1,t
r2 =
I2,t+1
I2,t
(2)
where:
I1,t :Texturally filtered image at a time t.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
162
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
I1,t+1 :Texturally filtered image at a time t+1.
I2,t :Original image at a time t.
I2,t+1 :Original image at a time t+1.
5.
PROPOSED APPROACH : FLOOD DETECTION
USING RATIO AND CHOERENCE MAP
This approach combined the ratio r and consistency γ of radar
images It and It+1 to determine any changes between areas and
therefore the extraction of flooded zones. In fact, since γ measured the similarity degree between these two images, any changes
occured during the flood event could be detected if the value of
γ is small. In this proposed appraoch, our main contribution is to
explore this assumption by using the coherence information between It and It+1 to improve the flood detection. In our study,
the sar images are acquired with the Envisat satellite, taken during and after the flood event.
The coherence image is simply the module of this complex coefficient γ (Giordano et al., 2005) .
γ = qP
PN
j=1
N
j=1
∗
It ()It+1
()
|It ()|2
PN
j=1
(3)
|It+1 ()|2
;
Figure 4: Change detection result using Cr in the case of Envisat
images .
flooding. We have calculated the M SE1 between flooding images determinate from optical data (Fig. 6-(a)), considered as a
reference image, and change detection image. The relationship
between the threshold values and the obtained MSEs is shown
in Fig. 5. M SE1 decrease when the threshold increases. Starting from a particular threshold, we obtain M SE1 = 0.15 and
M SE2 = 0.18 using mask method.
This is actually a picture of correlation between the two images
It and It+1 . Indeed, if two pixels are completely decorrelated,
the coefficient |γ| tends to 0. In the opposite scenario, coherence
approaches 1. N is the number of pixels whose analysis window
is equal to 3 (Liu et al., 2001). We propose to mode the change
detection coefficient as given by the following expression:
M SE1 =
N
M
1 XX
(Cs (i, j) − Iop (i, j))2
MN
(6)
N
M
1 XX
(M (i, j) − Iop (i, j))2
MN
(7)
i
M SE2 =
i
Cr =
1 It+1
r
=
γ
γ It
(4)
There are three possible cases:
Case 1: If the area undergoes no change then r and γ are close to
1 so the values of Cr is around 1.
Case 2: If the area undergoes negative changes (flooded areas)
then γ tends to 0 and r >> 1 so the values of Cr should be significant.
Case 3: If the area undergoes positive changes (non flooded areas) then γ tends to 0 and r s1 where s1 is a threshold to be determined. Threshold was chosen to minimize MSE (mean square error) between
image Cr and optical image, considered as land truth (Fig. 5).
M SE =
M
N
1 XX
(Cr (i, j) − Iop (i, j))2
MN
i
;
(5)
j
where Iop represents flooded areas mapping extracted from optical data.
Figure 5: Relationship between the threshold values and the MSE
of the NEST and the proposed approaches.
EXPERIMENTAL RESULTS AND DISCUSSIONS
Fig. 6-(a) shows the flooded zones in area 1 of the Kef test site in
2005 extracted using ETM+ optical data. Since each pixel of the
ETM+ image is 30×30 meters and the number of flooded pixel is
42104, the surface of the flooded area is calculated as following:
Exploited satellite database includes two ETM+ multispectral scenes:
Marsh 14th , 2005 in the raw, and May 1st , 2005 during the off
Sop = 42104 × 30 × 30 = 37.89 km2
6.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
(8)
163
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
Thus, flooded zones represent about 14.22% of the total land
area. Moreover, the spatial resolution of SAR images is 12.5 ×
12.5 meters. The flooding surfaces S1 and S2 obtained from the
mask method and from the method of change detection area respectively are:
order to extract flooded areas more precisely.
As future work, we can use the elevation information of the soil,
provided from the Digital Elevation Model (DEM) of the area of
interest, to prevent the flooded zones with low elevation.
REFERENCES
S1 = 30.65 km2 , S2 = 40.74 km2
(9)
This means that the flooded zone covers 11.5% of the total land
area for the method of mask and 15.28% for the method of change
detection.
Andreoli, R. and Yesou, H., 2007. Large scale change detection techniques dedicated to flood monitoring using envisat wide
swath mode data. Geoscience and Remote Sensing Symposium,
2007. IGARSS 2007. IEEE International pp. 2382–2385.
Giordano, F., Goccia, M. and Dellepiane, S., 2005. Segmentation of coherence maps for flood damage assessment. In: Image
Processing, 2005. ICIP 2005. IEEE International Conference on,
Vol. 2, IEEE, pp. II–233.
Lee, J.-S., Papathanassiou, K. P., Ainsworth, T. L., Grunes, M. R.
and Reigber, A., 1998. A new technique for noise filtering of sar
interferometric phase images. Geoscience and Remote Sensing,
IEEE Transactions on 36(5), pp. 1456–1465.
(a)
(b)
Liu, J. G., Black, A., Lee, H., Hanaizumi, H. and Moore, J. M.,
2001. Land surface change detection in a desert area in algeria using multi-temporal ers sar coherence images. International
Journal of Remote Sensing 22(13), pp. 2463–2477.
Next ESA, all rights reserved, n.d. http://nest.array.ca/.
of Environment, T. D. G. and of Life Branch, Q., 2006. Regional
action program for fighting against desertification in the region of
KEF. Ministry of Equipment, Land-Use Planning and Sustainable
Development.
of the Directorate General of Water Resources, P., 2005. Annuaire hydrologique de tunisie 2004-2005.
(c)
(d)
Rignot, E. J. and van Zyl, J. J., 1993. Change detection techniques for ers-1 sar data. Geoscience and Remote Sensing, IEEE
Transactions on 31(4), pp. 896–906.
Roque, D., Afonso, N., Fonseca, A. and Heleno, S., 2013. Building a database of flood extension maps using satellite imagery.
Proccedings of the ESA Living Planet Symposium, Edinburgh
September 9.
Silveira, M. and Heleno, S., 2008. Water/land segmentation in sar
images using level sets. In: Image Processing, 2008. ICIP 2008.
15th IEEE International Conference on, IEEE, pp. 1896–1899.
(e)
(f)
Figure 6: Flooded areas in black : (a) optical method, (b) coherence map, (c) mask method, (d) the difference image (a) − (c),
(e) the proposed approach and the difference image (a) − (e).
7.
CONCLUSION
The results presented in this paper demonstrate the relevance of
the combination of satellite imagery (optical and radar) for mapping flooded areas. Indeed, processing and analysis of a multitemporal series of optical data, ETM+, and SAR data of the study
area, acquired in different seasons, have identified a set of elements that can inform land uses in the area: vegetation units, soil
and water. Furthermore, the multitemporal comparison between
images was used to measure the surfaces of flooded areas around
the river Mellegue and the impact of flooding on other region of
the North-west of Tunisia. In this study, the ratio 4/3 of optical
bands allows the classification of land occupation. Furthermore,
we have combined the ratio of SAR bands and the coherence in
Silveira, M. and Heleno, S., 2009. Classification of water regions
in sar images using level sets and non-parametric density estimation. In: Image Processing (ICIP), 2009 16th IEEE International
Conference on, IEEE, pp. 1685–1688.
Van Leer, K. W. and Galantowicz, J., n.d. Comparisons of flood
affected area derived from modis and landsat imagery. pp. 1–8.
Zhou, G., Luo, J., Yang, C., Li, B. and Wang, S., 2000.
Flood monitoring using multi-temporal avhrr and radarsat imagery. Photogrammetric Engineering and Remote Sensing 66(5),
pp. 633–638.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
164
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
FLOOD MAPPING USING INSAR COHERENCE MAP
Slim Selmia , Wajih Ben Abdallaha , Riadh Abdelfattaha,b
a
COSIM Lab, University of Carthage, Higher School Of Communications of Tunis
Route de Raoued KM 3.5, Cite El Ghazala Ariana 2083 - Tunisia, email:[email protected]
b
D´epartement ITI, T´el´ecom Bretagne, Institut de T´el´ecom, Technopˆole Brest-Iroise CS 83818
29238 Brest Cedex 3 - France, email:[email protected]
KEY WORDS: Flood detection, SAR Data, InSAR coherence map.
ABSTRACT:
Classic approaches for the detection of flooded areas are based on a static analysis of optical images and/or SAR data during and after
the event. In this paper, we aim to extract the flooded zones by using the SAR image coupled with the InSAR coherence. A new
formulation of the ratio approach for flood detection is given considering InSAR coherence. Our contribution is to take advantage from
the coherence map provided using the InSAR pairs (one before and one after the event) to enhance the detection of flooded areas. We
explore the fact that the coherence values during and after the flood are mainly differents on the flooded zones and we give a more
suitable flood decision rule using this assumption. The proposed approach is tested and validated in the case of the flood taken place in
2005 in the region of Kef in Tunisia.
1.
INTRODUCTION
Earth has experienced in recent years, several natural disasters
that have an environment impact as well as various aspects of human life (Zhou et al., 2000). One of these remarkable events is
floods. In Tunisia, the risk of floods affects about two out of three
municipalities (of the Directorate General of Water Resources,
2005). It is therefore necessary to provide objective and comprehensive information about the area before, during and after
this hydrological event(Zhou et al., 2000). To guide support effectively and to provide quantifiable estimates of affected infrastructure and land extent, remote sensing techniques are used to
measure and monitor floods (Andreoli and Yesou, 2007). Indeed,
remote sensing data integration allows calculation and rapid assessment of water levels, damages and flood risk by area (Zhou
et al., 2000), (Andreoli and Yesou, 2007).
In this study, two main methods based on SAR (Synthetic Aperture Radar) data, for the extraction of flooded areas are studied
and compared. We then propose an optimization of the more
accurate one: the ratio approach (Andreoli and Yesou, 2007).
The first method is developed by NEST (Next ESA, all rights
reserved, n.d.) and it is based on the determination of a mask
from the color composite images taken before and during the
flood. Andreoli and Yesou developed an approach based on the
texture filtering (Andreoli and Yesou, 2007) and the detection of
the change through the bands ratio (Andreoli and Yesou, 2007).
In order to overcome the problem of decorrelation due to the
landslide caused by the flood, we used the InSAR coherence as
an additional information for texture filtering. Thus, flood mapping using ETM+ optical data is considered as a ”ground truth”
(Van Leer and Galantowicz, n.d.) for the validation and comparison of these methods.
2.
FLOODS IN NORTHWESTERN OF TUNISIA
We are interested in the region of Kef in the north-west of Tunisia
Fig. 1. This region is knowing for having a semi-arid climate and
it becomes inaccessible after floods. This will make very difficult
the task of research looking for exploring the test site within field
campaign after a flooding.
Figure 1: Location of the study area (36◦ 10′ 55′′ N, 8◦ 42′ 52′′ E).
According to the Tunisian hydrological calendar, published by
the Ministry of Agriculture and Water Resources (of the Directorate General of Water Resources, 2005), February is the month
of greatest rainfall. Table 1 shows data on precipitation in 2005.
Floods in Kef is taken place between February 14th and 15th2005 .
Researchers took more then 2 months (until April) in order to get
on the test site. This mainly due to the landslides caused by the
flooding.
In order to get a priori estimation of the flood mappping, we propose to explore different approaches based on SAR data. In this
paper, flood mapping using optical data will serve as reference
for the validation of results given from SAR approaches.
3.
FLOOD MAPPING USING OPTICAL DATA
A classic way to detect the flooded areas is based on the analysis of the optical images. The main idea is to test different processing satellite data to extract thematic land such as principal
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
161
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
Table 1: The amount of rain in 2005 in three rainfall stations in
the region of Kef.
Station
Mellegue
Zebida
K. Khasba
Jan
41.0
42.0
42
Feb
65.0
68.0
63.0
Mar
34.8
22.8
27.0
Apr
53.0
34.0
40.0
May
8.0
0.0
6.0
Jun
23.6
14.0
77.0
Jul
4.8
4.0
47.0
component analysis, the colored composition, multispectral classification and the band ratio 4/3 (Roque et al., 2013). Because
the Kef test site is mainly composed of soil, vegetation and water
(Roque et al., 2013), we opted for the choice of the band ratio
4/3 since it maximizes classes separability (of Environment and
of Life Branch, 2006).
The use of Landsat ETM+ tool for images processing and analysis has identified the most relevant elements of the landscape and
draw up land using maps (Fig. 2).
For our study, we used the Lee filter (Lee et al., 1998) to reduce
speckle noise and NEST tool (Next ESA, all rights reserved, n.d.)
to process image data and metadata. The radar sensors have a
high sensitivity to the dielectric constant and the surface roughness of observed objects. If we assume that a free water surface
has a very low roughness, we can consider it as specular reflector.
Thus, water is distinguished with a very low image backscatter
(Roque et al., 2013)(Silveira and Heleno, 2008).
4.1.1 Detection of flood using a mask To measure the extent
of floods, the permanent water surfaces shouldn’t be considered.
Otherwise, we will get erroneous results. This implies that we
need to hide some pixels and expose others. Before creating a
mask, the RGB image will be used where the red band denotes
the crisis image (February), the green band denotes the archive
1 and the blue band is or the archive 2 (Next ESA, all rights reserved, n.d.). From these images, we can see pixels associated
with flooded areas colored in purple. Knowing these values, and
based on histograms of images taken before and after the floods,
thresholds could be defined. Therefore, the permanent water surfaces could be detected using these thresholds. Using these data,
we can notice that the pixel values, in the case of the crisis image
(14 February), are below thresholds (from 250 to 350) but in the
case of the picture archive, the same pixels values are between
400 and 600. So the flooded areas satisfy the condition of N
mask (Fig. 3):
Figure 2: Optical image of river Mellegue taken by Landsat
ETM+ satellite on Mai 2005 with band ratios 4/3.
The pixels having values below 1.76 denote the surfaces of water.
Among water regions, some aren’t related to flooding (overflow
from the river due to the high flood waters and stagnant rain).
By examining the optical images taken over the region of Kef in
Tunisia on Marsh and May 2005, we can detect the flooded areas. The resulting map shows the areas that are affected during
the crisis. This will save for us as a ground truth for validation of
the developed approach. During the flood(in February) and immediately after the flood, optical data are not exploitable. Then,
SAR data in such situation are more adapted for flood mapping.
4.
FLOOD MAPPING USING SAR DATA
The radar data set used in this paper is provided by Envisat-2
satellite covering northwest of Tunisia and taken between February 2005 (during the flood) and May 2005 (after the flood).
4.1
Methodology
The methodology of radar data processing consists of three steps
(Andreoli and Yesou, 2007)(Silveira and Heleno, 2009):
• Geo-location of radar images
Figure 3: Flooded area (in red) around river Mellegue generated
with Envisat satellite image on Mars 2005 using NEST method.
N:
• Image interpretation and information extraction.
It < 350
It+1 > 550
(1)
where It and It+1 are the Envisat images taken during and after
the flood event repectively.
4.1.2 Detection of flooding using the ratio method The changing detector chosen for this method is based on the improvement
of the ratio that includes two levels of analysis (Roque et al.,
2013), the macro-scale analysis based on a first ratio r1 in order
to highlight changes of large homogeneous areas and combined
with a second ratio r2 obtained from two raw images that highlight key changes, while keeping a thematic accuracy of the raw
data (Roque et al., 2013)(Rignot and van Zyl, 1993).
r1 =
• Radiometric processing
I1,t+1
I1,t
r2 =
I2,t+1
I2,t
(2)
where:
I1,t :Texturally filtered image at a time t.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
162
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7, 2014
ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey
I1,t+1 :Texturally filtered image at a time t+1.
I2,t :Original image at a time t.
I2,t+1 :Original image at a time t+1.
5.
PROPOSED APPROACH : FLOOD DETECTION
USING RATIO AND CHOERENCE MAP
This approach combined the ratio r and consistency γ of radar
images It and It+1 to determine any changes between areas and
therefore the extraction of flooded zones. In fact, since γ measured the similarity degree between these two images, any changes
occured during the flood event could be detected if the value of
γ is small. In this proposed appraoch, our main contribution is to
explore this assumption by using the coherence information between It and It+1 to improve the flood detection. In our study,
the sar images are acquired with the Envisat satellite, taken during and after the flood event.
The coherence image is simply the module of this complex coefficient γ (Giordano et al., 2005) .
γ = qP
PN
j=1
N
j=1
∗
It ()It+1
()
|It ()|2
PN
j=1
(3)
|It+1 ()|2
;
Figure 4: Change detection result using Cr in the case of Envisat
images .
flooding. We have calculated the M SE1 between flooding images determinate from optical data (Fig. 6-(a)), considered as a
reference image, and change detection image. The relationship
between the threshold values and the obtained MSEs is shown
in Fig. 5. M SE1 decrease when the threshold increases. Starting from a particular threshold, we obtain M SE1 = 0.15 and
M SE2 = 0.18 using mask method.
This is actually a picture of correlation between the two images
It and It+1 . Indeed, if two pixels are completely decorrelated,
the coefficient |γ| tends to 0. In the opposite scenario, coherence
approaches 1. N is the number of pixels whose analysis window
is equal to 3 (Liu et al., 2001). We propose to mode the change
detection coefficient as given by the following expression:
M SE1 =
N
M
1 XX
(Cs (i, j) − Iop (i, j))2
MN
(6)
N
M
1 XX
(M (i, j) − Iop (i, j))2
MN
(7)
i
M SE2 =
i
Cr =
1 It+1
r
=
γ
γ It
(4)
There are three possible cases:
Case 1: If the area undergoes no change then r and γ are close to
1 so the values of Cr is around 1.
Case 2: If the area undergoes negative changes (flooded areas)
then γ tends to 0 and r >> 1 so the values of Cr should be significant.
Case 3: If the area undergoes positive changes (non flooded areas) then γ tends to 0 and r s1 where s1 is a threshold to be determined. Threshold was chosen to minimize MSE (mean square error) between
image Cr and optical image, considered as land truth (Fig. 5).
M SE =
M
N
1 XX
(Cr (i, j) − Iop (i, j))2
MN
i
;
(5)
j
where Iop represents flooded areas mapping extracted from optical data.
Figure 5: Relationship between the threshold values and the MSE
of the NEST and the proposed approaches.
EXPERIMENTAL RESULTS AND DISCUSSIONS
Fig. 6-(a) shows the flooded zones in area 1 of the Kef test site in
2005 extracted using ETM+ optical data. Since each pixel of the
ETM+ image is 30×30 meters and the number of flooded pixel is
42104, the surface of the flooded area is calculated as following:
Exploited satellite database includes two ETM+ multispectral scenes:
Marsh 14th , 2005 in the raw, and May 1st , 2005 during the off
Sop = 42104 × 30 × 30 = 37.89 km2
6.
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
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Thus, flooded zones represent about 14.22% of the total land
area. Moreover, the spatial resolution of SAR images is 12.5 ×
12.5 meters. The flooding surfaces S1 and S2 obtained from the
mask method and from the method of change detection area respectively are:
order to extract flooded areas more precisely.
As future work, we can use the elevation information of the soil,
provided from the Digital Elevation Model (DEM) of the area of
interest, to prevent the flooded zones with low elevation.
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S1 = 30.65 km2 , S2 = 40.74 km2
(9)
This means that the flooded zone covers 11.5% of the total land
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Figure 6: Flooded areas in black : (a) optical method, (b) coherence map, (c) mask method, (d) the difference image (a) − (c),
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CONCLUSION
The results presented in this paper demonstrate the relevance of
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This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XL-7-161-2014
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